Similarity-Based Clustering for Enhancing Image Classification Architectures
This work addresses efficiency issues in computer vision for researchers and practitioners, but it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of improving convolutional network performance by integrating content-based image similarity to enable clustered learning, resulting in reduced computational cost and increased evaluation speed.
Convolutional networks are at the center of best-in-class computer vision applications for a wide assortment of undertakings. Since 2014, a profound amount of work began to make better convolutional architectures, yielding generous additions in different benchmarks. Albeit expanded model size and computational cost will, in general, mean prompt quality increases for most undertakings but, the architectures now need to have some additional information to increase the performance. I show evidence that with the amalgamation of content-based image similarity and deep learning models, we can provide the flow of information which can be used in making clustered learning possible. The paper shows how training of sub-dataset clusters not only reduces the cost of computation but also increases the speed of evaluating and tuning a model on the given dataset.